Senior DevOps Engineer

Role Overview

We are looking for a Sr. DevOps Engineer to help build, operate, and continuously improve the secure cloud platforms that power Juvare’s mission-critical enterprise resilience solutions. In this role, you’ll partner across Engineering and Security to deliver reliable, scalable SaaS environments for commercial and federal customers, with a proven track record operating in regulated settings. Experience supporting AI/MLOps workflows is a plus.

Must Have

• Cloud Platforms: Strong hands-on experience with AWS and Azure
• Regulated Environments: Experience operating in FedRAMP High, DoD Impact Level 5 (IL5), or equivalent regulated environments
• Compliance & ConMon: Strong understanding of Authority to Operate (ATO) processes, compliance, and continuous monitoring (ConMon)
• GovCloud: Experience working within AWS GovCloud or secure boundary environments
• Databases: Operational experience with managed databases (backup/restore, replication, performance, and access management; Azure SQL and Aurora PostgreSQL preferred)
• Infrastructure as Code: Experience with infrastructure as code (Terraform)
• CI/CD Pipelines: CI/CD pipeline experience (GitLab, GitHub)
• Automation (AI-Enabled): Experience building workflow automation and developer automation using tools like n8n, Replit, or similar
• Containers & Orchestration: Strong experience with containers and orchestration (Docker, Kubernetes, AKS, EKS, ECS/Fargate)
• Monitoring & Observability: Experience with monitoring and observability (Datadog, CloudWatch)
• Security Tooling: Exposure to security tooling (WAF, SIEM, vulnerability management)
• Scripting: Strong scripting skills (Python, Bash, Powershell)
• Production Operations: Experience in production operations (incident response, on-call, RCA)
• Architecture: Strong understanding of networking, security, and system architecture
• AI / MLOps:
  • MLOps Pipelines: Experience with MLOps pipelines (training, deployment, monitoring)
  • Tools: Familiarity with tools like SageMaker, MLflow, Kubeflow, or similar
  • Model Deployment: Experience deploying models to production (real-time or batch)
  • Data & Lifecycle: Understanding of data pipelines and model lifecycle management

Nice to Have

• Experience with LLMs / GenAI workflows (RAG, prompt engineering, fine-tuning)
• Familiarity with vector databases (Pinecone, Weaviate, OpenSearch)
• Exposure to AIOps or AI-driven automation
• Experience with data platforms (Databricks, Snowflake, Redshift)
• GCP experience a plus; comfort operating with a cloud-agnostic mindset
• Multi-cloud or hybrid architecture experience
• Cost optimization and performance tuning